• We will be examining the response times of several “official” Twitter accounts: CalFire, Butte County Sheriff, and Chico Fire Department.
• Why is this important?
  • Because of the need of accurate, timely warnings and other information should a disaster, natural or otherwise, occur and threaten the lives and livelihoods of people.
• What about using television? Phone calls? Radio?
• How reliable is this information?
• What if you want more information?

• Television saw a 20% viewership decline between 2014-15 and 2018-19, averaging only 28.5 million viewers in PRIMETIME.
• Almost a third of all American households do not have a radio in their homes (Radio Ink, 2020).
• Twitter has an audience of 64.2 million users.
  • Not only is social media used by a wide variety of demographics, but it is slowly replacing traditional media outlets in terms of news distribution.
    • News now usually appears on social media first.
    • Social media crosses geographic boundaries more quickly.
    • Social channels are informed by multiple individuals firsthand, often through unofficial sources, but sometimes those who are direct witnesses to events.
    • Social media is commented upon by active social media users who further share and distribute content.
• Social media provides egalitarian access to the production and the consumption of news (Ceron, 2014).
• 77 percent of journalists say social media is important to them for learning about potential stories more quickly, and the same percentage say social media is important in reporting stories more quickly (Elliott 2018).
• During November of 2018 alone, CalFire’s official Twitter account saw an increase of almost 35k followers, showing that they were seen by the public as an important source of information (SocialBlade, 2020).

• Disaster Response and Twitter
  • Why is Twitter data important during disasters?
    • Twitter gives everyone a “real-time view” of disasters, allowing them to get information quickly from official sources regarding their safety (Ford, 2018).
  • “Given the volume of data produced, manually managing this process in the immediate aftermath of a crisis is not always practical. There is also often a need for unique updates related to particular topics within and across organizations” (Ford, 2018).
  • Some groups are coming up with programs specifically tailored towards combing Twitter during disasters for noun-verb pairs that might come up during disasters like “bridge collapsed” and “person trapped”. This could help official response times in future disasters (Ford, 2018).

• Potential Flaws in Phone-Based Warning Systems
  • “Call failure reports released by the Butte County Sheriff’s Office at the request of The Times show that the first evacuation orders requested by firefighters at the scene of the massive blaze frequently failed to connect. About as many calls went to voicemail as were answered by a live person (St. John and Serna, 2018).”
  • If officials are not able to make timely announcements and easily warn the public of a looming disaster, this presents a real problem within our system.
  • In the case of the Camp Fire, the police chief of Paradise said “there was no time for a citywide evacuation order — the city’s own system went down in the midst of a partial order” (St. John and Serna, 2018). This shows a need for a wide array of emergency notification methods, and with California being the state with the largest amount of Twitter users, the platform seems perfect to fit the role of one of these notification methods (Stirtz, 2020).

• Refiguration of disaster social relations
  • The proliferation of new communication technologies is replacing traditional media when it comes to the flow of information, there is a transformation in the visibility of disasters (Cottle 2014).
  • Plays a more progressive part in disasters: connected individuals are becoming more central in emergency responses in humanitarian crisis situations (Cottle 2014).
  • “Challenging the top-down, elite dominated communications” (Cottle 2014) There is a civilian surge of information as well as worldwide engagement because of fast-flowing information, collaboration between citizens, disaster relief agencies, volunteers, and public figures (Cottle 2014).

• Limits to crisis data
  • Once a disaster occurs, data collection starts. However, using social media platforms such as Twitter can create analytical and ethical oversights. Not everybody has access to the internet, where the disaster occurred there can be power outages, etc (Crawford and Finn 2015).
  • Before social media, traditional media was state-controlled, and governments were used to it, but that changed in more recent years (Crawford and Finn 2015).
  • The Twitter algorithm can influence the way tweets are created or how they are retweeted; “bots” can also impede data-collection (Crawford and Finn 2015).
  • People cannot control how their data is used, which can pose a privacy issue. It is important to examine underlying assumptions when using social or mobile data (Crawford and Finn 2015).

• How timely were the twitter accounts run by public officials and emergency services when it came to disseminating important and crucial information to the public during the Camp Fire? We will create a timeline of events to illustrate this problem.
• While there is a large amount of research that has been done regarding the usefulness of social media and its innovative uses in emergency management, the majority of the case studies have revolved around incidents such as earthquakes. In a study conducted by the US Department of Homeland Security in 2013, out of the six cases that were examined only one of them pertained to wildfires.
• One of the goals of our research is to examine the timeliness of social media in the role of emergency management by examining the response times of specific accounts along the timeline of the Camp Fire. Because the speed at which pertinent (and potentially life saving) information is so important during these types of events, we will be using a timeline to examine these response times more closely and compare them to the timeline of the actual disaster.

• 74,099 tweets were collected by the Spring 2020 Advanced Data Science class at CSU, Chico. Tweets were collected using the rtweet (Kearney MW 2019) package using R 3.6.2 (R Core Team) beginning the morning of November 11, 2018.
• A timeline of events from the morning of November 8, 2018 was derived from the Chico Enterprise-Record. (Epley)
• Each record in the data represents an individual tweet and contains all the information regarding the tweet in terms of who posted it, the times its been retweeted, and more. We are looking specifically into the time of day each tweet was posted to look into the response times of organizations in charge of monitoring the situation.
• During the data collection the only data that needed to be discarded were results that turned up not belonging to our query as they were not in English. Otherwise, our query that filtered tweets to include one or more of the following: #CampFire, #campfire, #paradise, #Paradise or @CALFIRE_ButteCo" during the correct time frame.
• The reliability of data collection is important to our research question as we are interested in few accounts and want to ensure we have the most information as possible and that it is accurate since we are concerned with such a small amount of data compared to the entirety of the dataset.
• We are observing tweets regarding seven public figures’ and organizations’ twitter accounts that were instrumental in dispersing information regarding the Camp Fire, specifically: @Cal_Fire, @CalFire_ButteCo, @ButteSherriff, @ChicoFD, @CountyOfButte, @Paradise_CA, and @ChicoPolice.
• The data is defined to be a media source when it belongs to one of the previous accounts, while all others are classified as public.

• Variable Creation: To successfully compare the response time of the following accounts: @Cal_Fire, @CalFire_ButteCo, @ButteSherriff, @ChicoFD, @CountyOfButte, @Paradise_CA, and @ChicoPolice to the real life timing of events we referenced Chico Enterprise-Record’s timeline of the fire. (Epley) We populated a data frame with each event and time as specified by Chico Enterprise-Record.
• Data Analysis: We analyzed the specific timing of emergency services and government entities’ tweets regarding pertinent information in relationship to the real life events through a timeline that incorporates both the twitter data and the derived timeline data in one merged timeline plot. We additionally created a timeline documenting the comparison between times of general public tweets and tweets that came from our selected local media sources stated above. Both timelines show the event description or tweet content along with the time the event or tweet occured.

• The data for the Camp Fire that we were most interested in involved initial response times, so we filtered down the dataset to only include tweets that occurred during the first six hours from the fire starting.
• The variables used throughout include:
  • Tweet content (text)
  • User’s Twitter Handle (screen_name)
  • Whether the tweet was a retweet (is_retweet)
  • Time the tweet was created (created_at_pst)
  • The events derived from Chico Enterprise-Record’s timeline (Epley)

In the following timeline each dot signifies a tweet or event to show tweet times in comparison to the real event times. The majority of the tweets shown are evacuation warnings and orders.

After eliminating retweets the following timeline compares the tweet frequency of the public vs the media for the morning up until 10:00 AM.

Cottle, Simon. (2014). Rethinking Media and Disasters in a Global Age: What’s Changed and Why It Matters. Media, War & Conflict. 7. 3-22.10.1177/1750635213513229. https://www.researchgate.net/publication/270633894_Rethinking_Media_and_Disasters_in_a_Global_Age_What's_Changed_and_Why_It_Matters

Elliott, Jennifer. “8 Best Practices for Emergency Communications on Social Media.” EfficientGov, 19 July 2018, https://www.efficientgov.com/community-engagement/articles/8-best-practices-for-emergency-communications-on-social-media-vwZS7OO5eoblrs3G/.

Epley, Robin (November 8, 2019). “Timeline: Breaking down Nov. 8 - the Day the Camp Fire Sparked.” Chico Enterprise-Record, Chico Enterprise-Record https://www.chicoer.com/2019/11/07/timeline-breaking-down-nov-8-the-day-the-camp-fire-sparked/.

Ford, Jordan. “Improving Disaster Response through Twitter Data.” Penn State University, 2018, https://news.psu.edu/story/527730/2018/07/10/research/improving-disaster-response-through-twitter-data.

Kearney MW (2019). “rtweet: Collecting and analyzing Twitter data.” Journal of Open Source Software, 4(42), 1829. Doi: 10.21105/joss.01829, R package version 0.7.0, https://joss.theoj.org/papers/10.21105/joss.01829.

R Core Team (2019). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org.

Radio Ink (March 3, 2020). “The Decline of the Home Radio”. https://radioink.com/2020/03/03/the-decline-of-the-home-radio/

Rice, Doyle (January 8, 2019). “USA had world’s 3 costliest natural disasters in 2018, and Camp Fire was the worst”. USA Today.

St. John, Paige, and Joseph Serna. “Camp Fire Evacuation Warnings Failed to Reach More than a Third of Residents Meant to Receive Calls.” Los Angeles Times, 1 Dec. 2018, https://www.latimes.com/local/california/la-me-ln-paradise-evacuation-warnings-20181130-story.html.

Stirtz, Kevin. “Twitter Ranking: Which States Twitter the Most?” All Business, Dun & Bradstreet, 14 May 2009, https://www.allbusiness.com/twitter-ranking-which-states-twitter-the-most-12329567-1.html.